Measure oriented cost-sensitive SVM for 3D nodule detection
- Peng Cao
- Dazhe Zhao
- Osmar R. Zaiane, University of Alberta (Database)
The class imbalance issue occurs when training a computer-aided detection (CAD) system for nodules. This imbalance causes poor prediction performance for true nodules. Moreover, the misclassification costs are different between two classes and high sensitivity of true nodules is essential in the detection. In order to eliminate or reduce the false positives while keeping high sensitivity, we present an effective wrapper framework incorporating the evaluation measure of imbalanced data into the objective function of cost sensitive SVM. We improve the performance of classification by simultaneously optimizing the best pair of misclassification cost parameter, feature subset and intrinsic parameters. We evaluated the method on a 3D Lung nodule dataset, showing that the proposed method outperforms many other exiting common methods, as well as specific imbalanced data learning methods, which indicates the effectiveness of our method on the imbalanced and unequal misclassification cost data classification.
Citation
P. Cao, D. Zhao, O. Zaiane. "Measure oriented cost-sensitive SVM for 3D nodule detection". Annual International Conference of the IEEE Engineering in Medicine and Biology Society, July 2013.| Keywords: | |
| Category: | In Conference |
| Web Links: | IEEE |
BibTeX
@incollection{Cao+al:EMBC13,
author = {Peng Cao and Dazhe Zhao and Osmar R. Zaiane},
title = {Measure oriented cost-sensitive SVM for 3D nodule detection},
booktitle = { Annual International Conference of the IEEE Engineering in
Medicine and Biology Society},
year = 2013,
}Last Updated: January 13, 2020Submitted by Sabina P
